Dialogue scenario classification based on social factors

Abstract

The tendency of interlocutors to become more similar to each other in the way they speak, this behavior is known in the literature as entrainment, accommodation, or adaptation. Previous studies indicated that entrainment can be treated as a social factor in human-human conversations. However, previous research suggests that this phenomenon has many subtleties. One of these cues is that entrainment on an acoustic feature might be associated with disentrainment on another in conversation, which means we have to consider these features together. Therefore, we proposed a linear dimensionality-reduction method that combines acoustic features to calculate three entrainment metrics: proximity, convergence, and synchrony. The three entrainment metrics are referred to as social factors hereafter. Our results show these social factors play an important role in a classification task. We also found that these social factors perform a better classification accuracy than combining each individual acoustic feature’s entrainment. The proposed social factors can help the human-machine interface to have the ability to adapt to the different scenarios in dialogue.